带色彩恢复因子的多尺度Retinex算法图像亮度增强代码

以下是带色彩恢复因子的多尺度Retinex算法图像亮度增强的Python代码：

import cv2
import numpy as np

def MSR(image, scales):
    weight = np.zeros_like(image)
    for scale in scales:
        blur = cv2.GaussianBlur(image, (0, 0), scale * 0.1)
        diff = cv2.absdiff(image, blur)
        diff = cv2.GaussianBlur(diff, (0, 0), scale * 0.1)
        weight += diff
    weight = (weight / len(scales)) + 1.0
    result = np.zeros_like(image)
    for i in range(3):
        result[:, :, i] = np.maximum(np.minimum((image[:, :, i] - weight[:, :, i] + 1.0) * 255.0, 255.0), 0.0)
    return result

def MSRCR(image, scales, radius, gamma):
    scales_1 = [0.25, 0.5, 1.0, 2.0, 4.0]
    scales_2 = [0.5, 1.0, 2.0, 4.0, 8.0]
    weights = []
    for scale in scales_2:
        weight = np.zeros_like(image)
        for radius in range(1, 6):
            weight += MSR(image, [scale]) * (gamma ** radius)
        weights.append(weight)
    weight = np.zeros_like(image)
    for i in range(3):
        weight[:, :, i] = np.mean([weight[:, :, i] for weight in weights], axis=0)
    weight = cv2.GaussianBlur(weight, (radius, radius), 0)
    result = np.zeros_like(image)
    for i in range(3):
        result[:, :, i] = np.maximum(np.minimum((image[:, :, i] - weight[:, :, i] + 1.0) * 255.0, 255.0), 0.0)
    return result

# 读取原始图像
image = cv2.imread("test.jpg")

# 调用MSRCR函数进行图像亮度增强
result = MSRCR(image, scales=[1.0], radius=15, gamma=1.0)

# 显示增强后的图像
cv2.imshow("Enhanced", result)
cv2.waitKey(0)
cv2.destroyAllWindows()

其中，`MSR`函数为多尺度Retinex算法，`MSRCR`函数为带色彩恢复因子的多尺度Retinex算法。在`MSR`函数中，`image`参数为原始图像，`scales`参数为用于计算权重的高斯核尺度列表。在`MSRCR`函数中，`image`参数为原始图像，`scales`参数为用于计算权重的高斯核尺度列表，`radius`参数为色彩恢复因子的高斯核半径，`gamma`参数为色彩恢复因子的衰减系数。最终，`result`为增强后的图像。